Read Me First — CSP / Wheel Pro

This lab follows the full economic cycle of cash-secured put selling and, when selected, the Wheel: cash → short put → possible assignment into shares → covered call → possible call-away. The important feature is that exposure changes by phase, so the strategy cannot be understood from premium income alone.

What this lab is designed to do

This lab follows the full economic cycle of cash-secured put selling and, when selected, the Wheel: cash → short put → possible assignment into shares → covered call → possible call-away. The important feature is that exposure changes by phase, so the strategy cannot be understood from premium income alone.

Key controls and inputs

• Strategy mode. Choose CSP-only when you want to study put selling without continuing into the covered-call phase, or the full Wheel when you want the complete assignment cycle.
• Put delta and DTE. These govern strike distance, premium, assignment probability, and how frequently the strategy resets.
• Market regime. Strong bull is especially important because a Wheel can spend time in cash and miss sustained upside. Bear and choppy regimes test assignment and recovery behavior.
• Cash collateral. The put should be genuinely cash secured. If the model lets idle cash earn a yield, include it consistently in both strategy and benchmark.
• Covered-call rule after assignment. In full-Wheel mode, the call parameters determine how quickly assigned shares may be called away and how much upside remains available.
• IV richness and transaction costs. Premium should be evaluated net of the volatility environment and repeated trading frictions.

What the outputs mean

• Time in cash / stock exposure. This explains much of the Wheel’s behavior and is critical when comparing it with buy-and-hold.
• Assignment frequency and phase transitions. Shows how often the strategy moves from put selling to share ownership and then to covered calls.
• Return and drawdown. Read these together with exposure. Lower drawdown may simply reflect spending more time in cash.
• Phase-matched control. Prefer this over a naive 100% stock comparison when the lab provides it.
• Reserve / collateral status. A strategy that requires more cash than assumed is not truly cash secured and should not be interpreted as the same strategy.

A good first experiment

1. Run the default full Wheel in a Typical regime with cash fully securing one put.
2. Record return, drawdown, assignment count, and time in cash.
3. Run the identical rule in a Strong bull regime. Observe the cost of waiting in cash and of capping upside after assignment.
4. Run it again in Choppy and Bear environments to see where premium and staged exposure may help or hurt.
5. Compare with the phase-matched stock-and-cash control rather than only 100% stock.
6. Change only put delta or DTE and repeat. If performance changes sharply, the apparent edge may be parameter-sensitive rather than robust.

How to interpret the result

Do not judge the strategy from one path, one seed, or one favorable market environment. Read return, drawdown, exposure, trade frequency, and benchmark-relative performance together. A result is more credible when it persists across reasonable parameter changes and when the comparison benchmark has similar economic exposure.

Important assumptions and limitations

• Assignment and option pricing follow the engine rules rather than broker-specific execution details.
• Cash-secured means enough cash is reserved to take assignment at the strike; leverage changes the strategy materially.
• The Wheel can appear safer because of lower average equity exposure. That is not the same as earning an options premium edge.
• Long bull markets are a structural challenge because cash drag and repeated call-away can produce large opportunity cost.
• Historical-underlying replay or bootstrap does not equal a historical option-chain backtest.

How this complements backtesting

A historical backtest tells you how the Wheel behaved through one realized sequence of prices and volatility. Monte Carlo and regime simulation let you ask whether the behavior survives different orders of returns, different bull/bear persistence, and different option-richness assumptions. The strongest evidence comes when conclusions are consistent across both approaches.